Optimizing machine translations for user engagement

ABSTRACT

Exemplary embodiments relate to techniques for improving a machine translation system. The machine translation system may include one or more models for generating a translation. The system may generate multiple candidate translations, and may present the candidate translations to different groups of users, such as users of a social network. User engagement with the different candidate translations may be measured, and the system may determine which of the candidate translations was most favored by the users. For example, in the context of a social network, the number of times that the translation is liked or shared, or the number of comments associated with the translation, may be used to determine user engagement with the translation. The models of the machine translation system may be modified to favor the most-favored candidate translation. The translation system may repeat this process to continue to tune the models in a feedback loop.

BACKGROUND

Machine translations involve the translation of information from asource language to a destination language via a computing device.Machine translations may be used to translate, for example,advertisements, government documents, academic works, text messages andemails, social networking posts, recordings of spoken language, andnumerous other works.

There may be more than one possible way to translate a word, phrase, orsentence into the destination language. Although each of these possibletranslations may be correct in certain circumstances, some translationsmay not make sense in the context of the full translation. For example,assume that the phrase “very good” is translated into German. The word“very” is typically translated as “sehr.” However, the word “good” maybe translated in different ways depending on the way that it is used.For example, the “good” in “good morning” is typically translated as“guten,” whereas the “good” in “that food is good” may be translated as“gut.” In this case, both “gut” and “guten” are reasonable translationsof the word “good,” but “sehr gut” is a more preferable translation than“sehr guten.”

Thus, multiple different translations may be generated for given sourcematerial. However, some possible translations may be more correct ormore favored than others. Identifying which translations are favored andcommunicating this information in a way that a machine translationsystem can consistently apply can be a difficult and time-consumingprocess.

One traditional technique for improving a machine translation involvesthe use of the Bilingual Evaluation Understudy (BLEU) score. In thistechnique, a segment such as a sentence of phrase is translated by amachine into a destination language. The machine-generated translationis compared to one or more reference translations, typicallygood-quality translations prepared by a human. A score between 0 and 1is assigned to the machine translation based on how well it approximatedthe human translation. When training a translation model for a machinetranslation system, the translation model may be evaluated in view ofthe BLEU score calculated over multiple translations, and modified toimprove its BLEU-score-based performance.

The BLEU score remains the industry standard in evaluating machinetranslations. However, several problems exist with techniques that relyon the BLEU score. One problem with the use of the BLEU score is that itcan be expensive to run evaluations. In order to accommodate differenttranslations that are nevertheless correct, multiple referencetranslations may be used. Because each reference translation istypically generated by a human, producing these reference translationscan be expensive and time-consuming. Moreover, there are questions as tohow well the BLEU score measures translation quality. Among otherissues, the BLEU score may not accurately capture whole sentence-levelmeaning, does not address grammatical correctness, and has difficultyevaluating translations involving languages that lack clear word-levelboundaries.

SUMMARY

Exemplary embodiments provide methods, mediums, and systems forimproving machine translations. According to exemplary embodiments, amachine translation system may receive source information in a sourcelanguage L_(a). The translation system may apply one or more models,such as a translation model and a language model, in order to generate aplurality of possible translations of the source information into adestination language L_(b).

The translation system may access a population of users, such as usersof a social network. The population may be divided into multiple groups,where each group receives one of the possible translations. Each user'sor group's engagement with the translation may be measured, whereengagement refers to the ways, positively or negatively, that the useror group interacts with the translation. In the above example involvingusers of a social network, for instance, different translations may besurfaced to different groups of users as different forms of a translatedarticle, post, or other type of content. Users may interact with thetranslations by clicking-through to read the article, “liking” a post,etc.

The models applied by the translation system may include one or morescores or parameters that are used to determine which words, phrases,sentences, etc. in the destination language L_(b) correspond to thesource information. The translation system may analyze the userinteractions to determine which translation received more userengagement (and/or more positive user engagement). If one translationreceives more user engagement than another, this may be used as anindication that the first translation was favored or a bettertranslation. Thus, based on the measured user interaction, thetranslation system may modify the scores or parameters of the models inorder to favor the translation having the higher user engagement. Thus,the models will be more likely to produce translations similar to theone that received greater engagement.

This procedure may be repeatedly applied in order to create a feedbacksystem in which multiple candidate translations are generated using amodel, the translations are evaluated for user engagement, the model ismodified to favor the translation having greater positive engagement,the updated model generates multiple candidate translations, and theprocess repeats.

These and other features and advantages will be described in more detailwith reference to the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1B depict a simplified overview of an exemplary system forimproving translation models based on user interaction with candidatetranslations;

FIG. 2 is a block diagram illustrating a translation system constructedaccording to an exemplary embodiment;

FIG. 3 is a flowchart depicting an exemplary process for improvingtranslation models based on user interaction with candidate translations

FIG. 4 is an example of a user interface for engaging with atranslation;

FIG. 5 is a flowchart depicting an exemplary process for measuring userengagement;

FIG. 6 depicts an exemplary network embodiment;

FIG. 7 describes the social networking graph depicted in FIG. 10 in moredetail.

FIG. 8 depicts an exemplary computing device suitable for use withexemplary embodiments.

DETAILED DESCRIPTION

Exemplary embodiments relate to techniques for improving a machinetranslation system by indirectly measuring the correctness of atranslation through user interaction with the translation. Rather thancomparing the translation to a human-generated translation andgenerating a score (such as the above-described BLEU score), exemplaryembodiments distribute multiple different candidate translations todifferent groups of users, and then measure user engagement with thedifferent candidate translations to determine which one is the mostfavored.

The users may be users of a social network. The users may interact withthe translations, such as by commenting on articles or posts includingtranslated material, liking an article or post including translatedmaterial, or sharing an article or post including translated material,among other possibilities. Metrics may be tracked to measure userinteraction, and the metrics may be provided to the translation system.In some embodiments, users may provide more direct feedback, such as byanswering a prompt asking whether a translation is usable orunderstandable.

The translation system may determine which of the candidate translationswas the most favored translation based on the metrics, and may modifythe translation system in order to favor the most favored translation.For example, the translation system may include one or more models thatare applied to generate translations, and the models may include one ormore parameters or scores that determine how given source material willbe translated. The machine translation system may modify the parametersor scores so that the system is more likely to generate translationssimilar to the most-favored candidate translation.

This process may be repeated in a feedback loop. Specifically, afterupdating the models, the translation system may apply the updated modelsto generate new candidate translations, new engagement information maybe generated, and the new translations may be evaluated to determinewhich is the most favored. The model may then be further updated in viewof the new engagement information.

By way of illustration, FIGS. 1A and 1B depict an example in which amachine translation system is modified based on user engagement withcandidate translations. As shown in FIG. 1A, a translation system 10includes a translation model 12, which is applied to translate an inputin a source language (L_(a)) into an output in a destination language(L_(b)). More specifically, the translation model 12 receives one ormore words, phrases, etc. in the source language L_(a) and generates alist of hypotheses that represent possible translations of the sourcelanguage material into the destination language L_(b).

The translation system 10 also includes a language model 14, whichselects between the different hypotheses to determine, in the context inwhich the hypotheses occur, which of the hypotheses is the more likelytranslation of the source material.

According to exemplary embodiments, the translation system 10 may applythe translation model 12 and the language model 14 to generate ncandidate translations (representing, e.g., the n translations that arethe highest-rated by the translation model 12 and/or the language model14). In the example of FIG. 1A, n=2. This results in a first candidatetranslation 16 and a second candidate translation 18.

The candidate translations are provided to a group of users, such asusers 22 of a social network 20. In this case, the first candidatetranslation 16 is provided to a first group 24, while the secondcandidate translation 18 is provided to a second group 26. Thetranslations may be surfaced to the users 22 of the social network 20.For example, the social network 20 may provide a news feed or other listof content which is presented to the users 22. In the news feed or listof content, the first group 24 may see the first candidate translation16, whereas the second group 26 may see the second candidate translation18.

Turning to FIG. 1B, the first group 24 may interact with the firstcandidate translation 16, while the second group 26 may interact withthe second candidate translation 18. These interactions might include,for example, commenting on a post containing the respective candidatetranslations, sharing the candidate translations, or liking thecandidate translations. The users 22 may also be asked to directlyevaluate the translation. For example, the users 22 may be asked whetherthe translation is useable or understandable. The social network 20 mayrecord metrics based on these interactions, and the metrics may be usedto generate a first engagement score 28 and a second engagement score30.

It is noted that, although FIG. 1B shows the local devices of the user22 generating the respective engagement scores 28, 30 and sending theengagement scores to the social network 20, the engagement scores couldbe calculated at the social network 20 and sent to the translationsystem 10, or could be calculated at the translation system 10. To thatend, the metrics may be provided to the social network 20 or to thetranslation system 10. Moreover, the social network 20 may receive themetrics and/or the engagement scores, and may use this information torank the candidate translations based on which was the mostinteracted-with translation. The social network 20 may send the ranking,or an indication of the most interacted-with translation, to thetranslation system 10.

Using this information, the translation system 10 may determine which ofthe candidate translations was the most-favored translation. Forexample, if the users 22 liked or shared the first candidate translation16 more than the second candidate translation 18, this may indicate thatit was easier to understand the first candidate translation 16. Thus, inthis example the first candidate translation 16 was the most favoredtranslation among the users 22.

The translation system 10 may be modified based on which translation wasthe most favored, so that the translation system 10 is more likely togenerate translations similar to the most favored translation in thefuture. For example, the translation model 12 may make use of one ormore translation parameters 32, while the language model 14 makes use ofone or more language parameters 34. The parameters 32, 34 may representone or more scores, model weights, etc. that affect which of multiplepossible translations are selected. The translations system 10 maymodify one or more of these parameters so that the translation model 12and/or the language model 14 are more likely to select the words orphrases that were used in the more favored candidate translation.

When another translation is requested of the translation system 10, thetranslation system 10 may apply the updated translation model 12 and theupdated language model 14. The above-described process may be repeatedin order to further fine-tune the translation system 10, thus creating afeedback loop in which the models 12, 14 are continually improved andused to generate new candidate translations.

These and other features of exemplary embodiments are described in moredetail below. Before further discussing the exemplary embodiments,however, a general note regarding data privacy is provided.

A Note on Data Privacy

Some embodiments described herein make use of training data or metricsthat may include information voluntarily provided by one or more users.In such embodiments, data privacy may be protected in a number of ways.

For example, the user may be required to opt in to any data collectionbefore user data is collected or used. The user may also be providedwith the opportunity to opt out of any data collection. Before opting into data collection, the user may be provided with a description of theways in which the data will be used, how long the data will be retained,and the safeguards that are in place to protect the data fromdisclosure.

Any information identifying the user from which the data was collectedmay be purged or disassociated from the data. In the event that anyidentifying information needs to be retained (e.g., to meet regulatoryrequirements), the user may be informed of the collection of theidentifying information, the uses that will be made of the identifyinginformation, and the amount of time that the identifying informationwill be retained. Information specifically identifying the user may beremoved and may be replaced with, for example, a generic identificationnumber or other non-specific form of identification.

Once collected, the data may be stored in a secure data storage locationthat includes safeguards to prevent unauthorized access to the data. Thedata may be stored in an encrypted format. Identifying informationand/or non-identifying information may be purged from the data storageafter a predetermined period of time.

Although particular privacy protection techniques are described hereinfor purposes of illustration, one of ordinary skill in the art willrecognize that privacy protected in other manners as well. Furtherdetails regarding data privacy are discussed below in the sectiondescribing network embodiments.

Next, an overview of the machine translation system is provided.

Machine Translation System

FIG. 2 is a block diagram overview of an example of a translation systemsuitable for use with exemplary embodiments. FIG. 2 depicts aunidirectional system in which a translation is performed from L_(a) toL_(b); however, the present invention is not so limited. The translationsystem may be bidirectional, performing translation in both directions(from L_(a) to L_(b) and from L_(b) to L_(a)). Moreover, amulti-directional system involving several languages L₁. L_(n) couldequally benefit from the present invention.

An input 36 may be provided to the machine translation system. The input36 may be in the form of text in the source language L_(a), such as textinput from a keyboard via a web browser or application. The input 36 mayalso take other forms, such as an audio recording, writing provideddirectly to a computing system through a stylus or electronic pen,writing indirectly provided to a computing system (e.g., by scanning ahandwritten or typed document), a photograph (e.g., a photograph of asign), and other suitable types of input. In order to provide the input26, a user may interact with the system via a graphical user interfacedisplayed on a computing device screen (or active touch screen), apointing device such as a mouse or pen, a microphone, and/or a keyboard.

In some embodiments, the translation system 10 is operable to translatetextual information from the source language L_(a) to the destinationlanguage L_(b). Accordingly, in order to handle multiple different typesof inputs 36, logic may be provided for converting the input 36 intotext. For example, FIG. 2 depicts automatic speech recognition (ASR)logic 38 that is configured to convert input audio in the sourcelanguage L_(a) into text in the source language L_(a). In order toconvert an audio recording to text, the ASR logic may utilize anacoustic model, an ASR class-based language model, and a recognitionlexicon model. One example of suitable ASR logic is the “Ninja” speechrecognizer system developed at Mobile Technologies, LLC. Other types ofASR logic that may be used include speech recognizers developed by IBMCorporation, SRI, BBN, Cambridge, or Aachen.

Other types of logic may be provided for other types of inputs 36 (e.g.,optical character recognition logic for converting input handwriting ortyping, image analysis logic for converting input photographs, etc.). Ifthe translation system operates on something other than text (e.g.,audio), suitable logic may be provided for converting the input 36 intoa format recognizable to the translation system.

The input 36 is provided to a translation system 10 (potentially afterbeing processed by the ASR logic 38 or other suitable logic). Thetranslation system 10 is configured to translate the input 36 from thesource language L_(a) into the destination language L_(b). Examples oftranslation systems 10 suitable for use with exemplary embodimentsinclude the “PanDoRA” system developed at Mobile Technologies, LLC, aswell as machine translation systems developed by IBM Corporation, SRI,BBN or at Aachen University.

Generally, the translation system 10 applies a translation model 12 tosource language words, phrases, sentences, etc. in the input 36 in orderto develop a list of plausible candidate destination language words,phrases, sentences, etc. that may represent a translation of the sourcelanguage material. The list of candidate destination language words,phrases, sentences, etc. are referred to as translation hypotheses.After generating a list of hypotheses, the list may be subjected tofurther analysis by a language model 14. The language model 14 considersthe context in which the hypotheses are used in the destination languageL_(b), and selects one of the hypotheses as the most likely translationof the source material.

The translation model 12 may be, for example, a phrase table withentries for each hypothesis. Each entry may include a source languageword, phrase, sentence, etc. and a paired destination language word,phrase, sentence, etc. Each entry may be associated with a score thatrepresents the likelihood, in general, that the destination languageportion of the pair is the translation of the source language portion ofthe pair. For illustration purposes, an example of a phrase table isshown in Table 1, below.

TABLE 1 Source Material Destination Material Score Good Gut 0.7 GoodGuten 0.5 Good Heiligmäβig 0.1

The source/destination pairs in the phrase table may be generated frombilingual training data 40. The bilingual training data 40 may includewords, phrases, sentences, etc. that have been previously translatedfrom the source language L_(a) to the destination language L_(b) (orvice versa). The score in the phrase table may represent a frequency atwhich the source/destination pairs were found to correspond to eachother in the bilingual training data 40. A translation trainer 42include logic to analyze the bilingual training data 40 and create ormodify entries in the phrase table based on the analysis.

As noted above, the translation model 12 produced by the translationtrainer 42 may be well-suited to generating an initial list ofhypotheses indicative or possible translations for the source material.However, the translation model 12 typically does not take context intoaccount. For example, Table 1 above shows that, in general, the word“Good” was translated into “Gut” slightly more often than it wastranslated into “Guten;” nonetheless, both are reasonably plausiblehypotheses. Thus, without any context, it may be difficult to determinewhich translation is the most likely. However, assume that the previousword was translated as “Sehr” (“Very”). In German, it is much morelikely that the word after “Sehr” should be translated as “Gut,” ratherthan “Guten.” In order to take this information into account, a languagemodel 14 provides one or more tuning scores that allow the initialphrase table score to be supplemented or replaced in view of the wordsand phrases surrounding a particular candidate hypothesis. Whenpresented with new information for translation, the translation system10 may generate an initial list of hypotheses using the translationmodel 12, and then may select the most likely translation from among thelist of hypotheses using the tuning scores provided by the languagemodel 14.

The language model 14 used to translate a source language L_(a) into adestination language L_(b) is a language model 14 for the destinationlanguage L_(b). The language model 14 may be trained using monolingualtraining data 44 for the destination language L_(b). The monolingualtraining data 44 may be any suitable list of words, phrases, sentences,etc. from the destination language L_(b). For example, the monolingualtraining data 44 may include publications, articles, or literature fromthe destination language L_(b), and/or may include text collected fromvarious sources (e.g., social networking posts, assuming that theauthor's privacy settings allow for the collection of such data). Alanguage trainer 46 may include logic configured to analyze themonolingual training data 44 and to generate one or more tuning scoresbased on the occurrence of words, phrases, etc. based on their placementwith respect to one another.

In some embodiments, a correction and repair module 48 employingcorrection logic may be provided. The correction and repair module 48allows the user to correct the translation system 10 output via multiplemodalities; including speech, gesture, writing, tactile touch-sensitiveand keyboard interfaces, and enables the system to learn from the user'scorrections. The correction and repair module may be of the type such asthat disclosed in U.S. Pat. No. 5,855,000.

User field customization logic may provide an interface for users to addnew vocabulary to the system, and can also select an appropriate systemvocabulary for their current situation. For example, a change in systemvocabulary may be triggered by a change in location, as determined bythe GPS coordinates indicating the current location of the user'sdevice, or an explicit selection of task or location by the user.

The Correction and Repair Module 48 records and logs any corrections theuser may make, which can be later used to update ASR logic 38 andtranslation system 10. If the correction contains a new vocabulary item,or if the user enters the field customization mode to explicitly add anew word to the system, or if a new word is automatically detected inthe input audio using confidence measures or new word models, such asthe method described in Thomas Schaaf, “Detection of 00V words usinggeneralized word models and a semantic class language model,” in Proc.of Eurospeech 2001, the new vocabulary item or word may be added to thetranslation model 12 and/or the language model 14.

After applying the translation model 12 and/or the language model 14 tothe input 36, the translation system 10 may generate an output 50 in thedestination language L_(b). The output 50 may be in a textual format andmay be presented on a display device some embodiments, the output 50 maybe automatically presented (e.g., an automatic translation or“autotranslation”). In other embodiments, a prompt may be presented andthe user may request that the translation be shown. The translation mayremain hidden until the user manually requests that the translation bepresented.

If it is desirable to provide the output 50 in a format other than text,then logic may be employed for converting the output 50 into the desiredformat. For example, FIG. 2 depicts text-to-speech (TTS) logic 52 forconverting the text generated by the translation system 10 into an audiorecording. The TTS logic 52 generates audio output for an output device,such as a speaker. Examples of suitable TTS logic 52 include theCepstral TTS module was used. Other TTS modules, such as TTS moduleswhich support Windows SAPI (speech application programming interface)conventions, could also be employed.

Model Training and Updating

FIG. 3 depicts an exemplary process for improving translation modelsbased on user interaction with candidate translations. The proceduredepicted in FIG. 3 may be applied as model training and updating logicincluding computer-executable instructions. The instructions may beexecuted by one or more trainers, such as the translation trainer 42and/or the language trainer 46.

The procedure begins at step 54, where the translation system 10receives an input in a source language L_(a). The input may be receivedas part of a request from a social network that a translation of theinput into a destination language L_(b) be generated. However, even inembodiments in which a social network is employed to measure userengagement with translations, the original input need not necessarilyoriginate in a social network.

At step 56, the translation system 10 may apply a translation model 12to the input, in order to generate two or more hypotheses in thedestination language L_(b) that represent translated words, phrases,etc. from the input. For example, the translation system 10 may retrievea phrase table (Table 1, above) from memory, where the phrase tableincludes pairs of words, phrases, etc. One member of the pair mayrepresent a word, phrase, etc. in the source language L_(a) and onemember may represent a word, phrase, etc. in the destination languageL_(b). The destination-side word, phrase, etc. may represent ahypothesis for a translation of the source-side word, phrase, etc. intothe destination language L_(b).

The translation model 12 may include one or more scores or otherparameters that indicate a likelihood, in general, that the source-sideof the pair is translated into the destination-side of the pair. Thetranslation model may select the h most-likely phrase table entries thatrepresent possible translations of the source material. h may representany integer greater than one, and the specific number may be varieddepending on the application.

At step 58, the h most-likely hypotheses may be sent to the languagemodel 14, which may rank the h hypotheses based on tuning scores orother parameters used by the language model 14. The language model 14considers how the destination words, phrases etc. are used in thebroader context of the translated material, and is generally used toselect the single most likely word, phrase, etc. that represents thetranslation of the source material into the destination language L_(b).In contrast, in exemplary embodiments the language model 14 selects then most likely hypotheses at step 60, and generates n candidatetranslations (one for each hypothesis) at step 62. n may represent anyinteger greater than one, and the specific number may be varieddepending on the application.

In some cases, the n candidate translations may differ based on a singleword or phrase (e.g., each differing based on only a single differententry in the phrase table). In other embodiments, the n candidatetranslations may differ in multiple words or phrases (e.g., eachemploying multiple different phrase table entries). In this case, theanalysis of user engagement may take into account the multiple differentselections that went into the n candidate translations (e.g., comparinguser engagement across multiple sets of variables to isolate whichvariable or variables was most responsible for the user engagementscore, and/or determining whether two or more variables interacted witheach other).

At step 64, the translation system 10 may cause the n candidatetranslations to be provided to a group of users. In one embodiment, then candidate translations are provided to a social network fordistribution to different groups of social network users. The ncandidate translations may be surfaced to the different groups (e.g., bydisplaying the different translations in the users' news feeds).

At step 66, the translation system 10 may receive interaction metrics,engagement scores, or a ranking of the n candidate translations from thesocial network. Based on the received information, at step 68 thetranslation system 10 may determine which of the n candidatetranslations was the most-favored translation. In some embodiments, thetranslation system 10 may identify multiple favored translations.

For example, the translation system 10 may receive metrics such as anumber of likes, a number of shares, and/or a number of commentsassigned to each translation. The translation system may add thesemetrics together and/or may weigh the value assigned to each of themetrics to generate an engagement score.

Furthermore, users may be asked to directly evaluate a translation'squality. The present inventors have learned that asking a user to “rate”a translation often yields inconsistent results, because a user may notknow on what basis they should be rating the translation. Moreover,different users will apply different rating scales. Instead, it has beenfound that asking a user whether a translation was “useable” or“understandable” produced more consistent and more useful results. Sucha user-generated understandability rating may be considered along withthe other metrics.

Optionally, the metrics may include data indicating demographicinformation for the user that generated the metric (e.g., the metricsmay indicate the age, gender, nationality, etc. of a user that shared,liked, or commented on the translation). Different engagement scores maybe calculated for different target groups. This may allow for multipledifferent models 12, 14 to be generated, one for each demographic groupof interest. As a result, different translations may be generated basedon the language patterns of different demographic groups, and anappropriate translation may be provided based upon an identity of a userrequesting the translation, or a target group identified in thetranslation request.

The metrics may be normalized depending on the demographic group basedon the demographic group's language capabilities or preferences. Forexample, a demographic group containing young people may be moretolerant of poor translations than a demographic group including olderpeople. Similarly, a demographic group of non-nativedestination-language speakers may be less tolerant of poor translationsthan a demographic group of native destination-language speakers.

At step 68, the engagement scores or rankings may be evaluated todetermine one or more favored translations from among the candidatetranslations. For example, the candidate with the highest engagementscore may be selected as the favored translation, or the 2, 3, or morehighest-ranked candidates may be selected.

At step 70, the translation system 10 may modify the translation model12 and/or the language model 14 to favor the favored translation(s). Forexample, the translation system 10 may identify one or more phrase tableentries that were applied to generate the favored translation(s) thatwere not applied in the non-favored translation(s). The translationsystem 10 may increase the scores or parameters from these entries, ormay alter the tuning scores applied by the language model 14, toincrease the likelihood that these entries will be selected in thefuture. The translation system 10 may also (or as an alternative)decrease the scores of non-favored entries. The updated model(s) may bestored in memory and may replace a previous version of the model.

At step 72, the translation system 10 may determine whether thetranslation model 12 and/or the language model 14 have entered a stablestate. For example, if the parameters modified at step 70 were changedby less than a predetermined threshold amount (possibly over the courseof several loops through the process of FIG. 3), then the models may beconsidered to be stable and further tuning is not required. Thus, atstep 74, processing may end.

If it is determined at step 72 that the models have not entered a stablestate, then processing may return to step 54, and new candidatetranslations may be generated by the updated models. The new candidatetranslations may be evaluated for user engagement and used to furtherrefine the models.

Measuring User Engagement

As noted above, different translations may be presented, or surfaced, todifferent groups of users, and the users may engage with the translationin different ways. FIG. 4 is an example of a user interface for engagingwith a translation.

A user's device, such as a mobile device may include a display area 76.The display area 76 may display an interface for an application, such asa social networking application. Among other possibilities, the displayarea 76 may include a display of a news feed 78 showing recent activityon the social network which the social network has determined may be ofinterest to an active user.

For example, the news feed depicted in FIG. 4 shows a recent post 80that the current user's friend has recently written. The post 80 issurfaced to the current user in the user's newsfeed. The post 80 mayinclude translated information. For example, the posting user may havewritten the original post in English, but the post 80 appearing in thenews feed 78 may appear in Spanish. This may occur for a number ofreasons. The social network may have identified that the current user isa Spanish speaker (e.g., through user-defined language preferences or byauto-detecting the language used by the current user). In this case, thesocial network may automatically translate non-Spanish posts intoSpanish for the current user. In other embodiments, the post 80 mayoriginally appear in the native language in which it was written, and anoption may be presented for translating the post into a destinationlanguage.

The current user can engage with the post in a number of ways. Forexample, a “like” button 82 is associated with the post. By interactingwith the like button 82, the current user indicates their approval ofthe post 82 (typically, interacting with the “like” button wouldincrease the count of “likes” displayed in connection with the post 80and show other users that the current user had liked the post 80).

The user may also interact with a share button 84. Interacting with theshare button 84 causes the post 80 to be distributed to one or moredestinations identified by the current user. For example, the currentuser may select a group of people to whom the post should be sent, ormay cause the post to appear 80 on the current user's own collection ofpostings.

Moreover, the user may comment on the post through a comments field 86.If the user has the appropriate permissions, commenting on the post 80will cause the text entered in the comments field 86 to appear inconnection with the post 80.

Although several specific ways of interacting with the post 80 have beendescribed, one of ordinary skill in the art will recognize that theremay be other ways to interact with translated material.

As shown in FIG. 4, it is not necessary to indicate to the user that thepost 80 is a machine-generated translation. Nonetheless, in someembodiments, the display area 76 may include an indication that the post80 is a translation, and an option may be presented allowing the user toindicate if the translation is understandable or useable. Thisinformation may also be recorded as part of the user engagement metrics.

The interface depicted in FIG. 4 may be employed in connection with asocial network for measuring user engagement. FIG. 5 depicts anexemplary process for measuring user engagement.

At step 88, a social networking service may access source material in asource language L_(a). For example, the source material may be a user'spost, an article or publication, a web page, or any other materialwritten in the source language. Among other possibilities, the socialnetworking service may receive a request to translate the sourcematerial into a destination language.

At step 90, the social networking service may transmit a request to atranslation system to translate the source material into the destinationlanguage. The destination language may be indicated in the originalrequest to translate the source material received by the socialnetworking service, or may be determined automatically by the socialnetworking service (e.g., based on user preferences or an automaticallydetermined language of the user).

At step 92, the social networking service may receive n candidatetranslations in response to the request sent at step 90. The socialnetworking service may identify a corresponding n user groups at step94. The n user groups may be made up of a random sampling of users thatare likely to be interested in the source material, or may include anon-random selection of users (e.g., to ensure a diversity ofdemographic groups, or to test different translations within a givendemographic group). In some embodiments, the n user groups may not beidentified immediately, but rather the social networking service maywait to receive requests for the translated source material. As eachrequest is received, the social networking service may provide adifferent translation, e.g. in round-robin fashion or by providing arandom translation candidate.

At step 96, the social networking service may surface the translationsto the users in the user groups identified in step 94. For example, thetranslations may be provided to a user's news feed, may appear in abanner or advertisement, or otherwise may be made available to the user.Optionally, at step 98, the social networking service may present auseability prompt along with the translation asking the user whether thetranslation is useable or understandable.

At step 100, the social networking service may record user engagementmetrics each time that one of the users of the n user groups interactswith the translation. This may include actions such as liking, sharing,or commenting on the translation.

Optionally, at step 102 the social networking service may calculate anengagement score based on the metrics recorded at step 100. For example,after the candidate translation has been exposed for a predeterminedperiod of time, the social networking service may add up the number oflikes, shares, and comments, may weigh the different categories ofengagement types based on a relative importance of the categories, andmay normalize the result. This result may be an engagement score thatrepresents how much, how often, or how deeply users interacted with thecandidate translation. The engagement score may be normalized so that itmay be compared to the engagement scores of the other candidatetranslations.

Optionally, at step 104 the social networking service may generate aranking of the candidate translations based on the amount or depth ofinteractions that the users had with the candidate translations.

It is noted that steps 102 and 104 need not necessarily be performed bythe social networking service. For example, the metrics could be sentdirectly to a translation system 10 or third-party service andengagement scores or rankings could be calculated there.

It is further noted that the metrics, engagement scores, and rankingsneed not be calculated based on user-by-user data. In somecircumstances, it may be possible to calculate these scores, forexample, on a translation-by-translation basis. For example, differentcandidate translations may be stored at different locations and accessedby different links or uniform resource locators (URLs). The socialnetwork may count the number of times that the link or URL is accessedfor each candidate translation, which may serve as a proxy for userengagement.

At step 106, the social networking service may transmit the metrics,engagement score, and/or ranking to the translation system 10.Processing may then end at step 108.

Network Embodiments

Some exemplary embodiments may be employed in a network environment,such as the environment depicted in FIG. 6.

A user may interact with a client 210, which may be (for example) apersonal computer, tablet, mobile phone, special-purpose translationdevice, etc. In some embodiments, the client 210 does not requireinteraction from a user.

The client 210 may include one or more input devices 212 and one or moreoutput devices 214. The input devices 212 may include, for example,microphones, keyboards, cameras, electronic pens, touch screens, andother devices for receiving an input in a source language L_(a). Theoutput devices 214 may include a speaker, a display device such as amonitor or touch screen, and other devices for presenting an output in adestination language L_(b).

In some embodiments, the input from the input devices 212 may be in theform of an input 36 that is being sent to a translation system 10 fortranslation. In other embodiments, the client 210 may also submittraining data, a phrase table, a translation, or a translation and theoriginal source data used to generate the translation.

The client 210 may include a memory 216, which may be a non-transitorycomputer readable storage medium, such as one or a combination of a harddrive, solid state drive, flash storage, read only memory, or randomaccess memory. The memory 216 may a representation of an input 36 and/ora representation of an output 50, as well as one or more applications.For example, the memory 216 may store a social networking client 218that allows a user to interact with a social networking service.

The input 36 may be textual, such as in the case where the input device212 is a keyboard. Alternatively, the input 36 may be an audiorecording, such as in the case where the input device 212 is amicrophone. Accordingly, the input 26 may be subjected to automaticspeech recognition (ASR) logic 38 in order to transform the audiorecording to text that is processable by the translation system 10. Asshown in FIG. 10, the ASR logic 38 may be located at the client device210 (so that the audio recording is processed locally by the client 210and corresponding text is transmitted to the translation server 224), ormay be located remotely at the translation server 224 (in which case,the audio recording may be transmitted to the translation server 224 andthe translation server 224 may process the audio into text). Othercombinations are also possible—for example, if the input device 212 is atouch pad or electronic pen, the input 36 may be in the form ofhandwriting, which may be subjected to handwriting or optical characterrecognition analysis logic in order to transform the input 36 intoprocessable text.

Similarly, a resulting output 50 from a translation system 10 may be inthe form of text. In some embodiments, the desirable end form of theoutput may be something other than text, such as an audio representationof the translation. Accordingly, the output 50 may be subjected totext-to-speech (TTS) logic 52 in order to transform the text into anaudio recording that is presentable by the output devices 214. As shownin FIG. 10, the TTS logic 52 may be located at the client device 210 (sothat the output text is processed locally by the client 210 andcorresponding audio is sent to the output devices 214), or may belocated remotely at the translation server 224 (in which case, text maybe processed at the translation server 224 and the resulting audiorecording may be transmitted to the client 210). Other combinations ofprocessing logic are also possible, depending on the desired final formfor the output 50.

The client 210 may be provided with a network interface 220 forcommunicating with a network 222, such as the Internet. The networkinterface 220 may transmit the input 10 in a format and/or using aprotocol compatible with the network 222 and may receive a correspondingoutput 28 from the network 222.

The network interface 220 may communicate through the network 222 to atranslation server 224. The translation server 224 may host theabove-described translation system 10. The translation system 10 maytranslate the input 36 into an output 50.

The network interface 220 of the client 210 may also be used tocommunicate through the network 222 with a social networking server 226.The social networking server 226 may include or may interact with asocial networking graph 228 that defines connections in a socialnetwork. Furthermore, the translation server 224 may connect to thesocial networking server 226 for various purposes, such as retrievingtraining data from the social network.

A user of the client 210 may be an individual (human user), an entity(e.g., an enterprise, business, or third-party application), or a group(e.g., of individuals or entities) that interacts or communicates withor over the social-networking server 226. The social-networking server226 may be a network-addressable computing system hosting an onlinesocial network. The social-networking server 226 may generate, store,receive, and send social-networking data, such as, for example,user-profile data, concept-profile data, social-graph information, orother suitable data related to the online social network. Thesocial-networking server 226 may be accessed by the other components ofthe network environment either directly or via the network 222.

The social-networking server 226 may include an authorization server (orother suitable component(s)) that allows users to opt in to or opt outof having their actions logged by social-networking server 226 or sharedwith other systems (e.g., third-party systems, such as the translationserver 224), for example, by setting appropriate privacy settings. Aprivacy setting of a user may determine what information associated withthe user may be logged, how information associated with the user may belogged, when information associated with the user may be logged, who maylog information associated with the user, whom information associatedwith the user may be shared with, and for what purposes informationassociated with the user may be logged or shared. Authorization serversmay be used to enforce one or more privacy settings of the users ofsocial-networking server 226 through blocking, data hashing,anonymization, or other suitable techniques as appropriate.

More specifically, one or more of the content objects of the onlinesocial network may be associated with a privacy setting. The privacysettings (or “access settings”) for an object may be stored in anysuitable manner, such as, for example, in association with the object,in an index on an authorization server, in another suitable manner, orany combination thereof. A privacy setting of an object may specify howthe object (or particular information associated with an object) can beaccessed (e.g., viewed or shared) using the online social network. Wherethe privacy settings for an object allow a particular user to accessthat object, the object may be described as being “visible” with respectto that user. As an example and not by way of limitation, a user of theonline social network may specify privacy settings for a user-profilepage identify a set of users that may access the work experienceinformation on the user-profile page, thus excluding other users fromaccessing the information. In particular embodiments, the privacysettings may specify a “blocked list” of users that should not beallowed to access certain information associated with the object. Inother words, the blocked list may specify one or more users or entitiesfor which an object is not visible. As an example and not by way oflimitation, a user may specify a set of users that may not access photosalbums associated with the user, thus excluding those users fromaccessing the photo albums (while also possibly allowing certain usersnot within the set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or content objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 204 corresponding to a particular photo may havea privacy setting specifying that the photo may only be accessed byusers tagged in the photo and their friends. In particular embodiments,privacy settings may allow users to opt in or opt out of having theiractions logged by social-networking system 100 or shared with othersystems (e.g., third-party system 170). In particular embodiments, theprivacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, and my boss), userswithin a particular degrees-of-separation (e.g., friends, orfriends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableusers or entities, or any combination thereof. Although this disclosuredescribes using particular privacy settings in a particular manner, thisdisclosure contemplates using any suitable privacy settings in anysuitable manner.

In response to a request from a user (or other entity) for a particularobject stored in a data store, the social-networking system 226 may senda request to the data store for the object. The request may identify theuser associated with the request. The requested data object may only besent to the user (or a client system 210 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store, or may prevent the requested object frombe sent to the user. In the search query context, an object may only begenerated as a search result if the querying user is authorized toaccess the object. In other words, the object must have a visibilitythat is visible to the querying user. If the object has a visibilitythat is not visible to the user, the object may be excluded from thesearch results.

In some embodiments, targeting criteria may be used to identify users ofthe social network that may benefit from the above-described translationsystem. Targeting criteria used to identify and target users may includeexplicit, stated user interests on social-networking server 226 orexplicit connections of a user to a node, object, entity, brand, or pageon social-networking server 226. In addition or as an alternative, suchtargeting criteria may include implicit or inferred user interests orconnections (which may include analyzing a user's history, demographic,social or other activities, friends' social or other activities,subscriptions, or any of the preceding of other users similar to theuser (based, e.g., on shared interests, connections, or events)).Particular embodiments may utilize platform targeting, which may involveplatform and “like” impression data; contextual signals (e.g., “Who isviewing now or has viewed recently the page for COCA-COLA?”);light-weight connections (e.g., “check-ins”); connection lookalikes;fans; extracted keywords; EMU advertising; inferential advertising;coefficients, affinities, or other social-graph information;friends-of-friends connections; pinning or boosting; deals; polls;household income, social clusters or groups; products detected in imagesor other media; social- or open-graph edge types; geo-prediction; viewsof profile or pages; status updates or other user posts (analysis ofwhich may involve natural-language processing or keyword extraction);events information; or collaborative filtering. Identifying andtargeting users may also implicate privacy settings (such as useropt-outs), data hashing, or data anonymization, as appropriate.

FIG. 7 illustrates an example of a social graph 228. In exemplaryembodiments, a social-networking service may store one or more socialgraphs 228 in one or more data stores as a social graph data structurevia the social networking service.

The social graph 228 may include multiple nodes, such as user nodes 230and concept nodes 232. The social graph 228 may furthermore includeedges 234 connecting the nodes. The nodes and edges of social graph 228may be stored as data objects, for example, in a data store (such as asocial-graph database). Such a data store may include one or moresearchable or queryable indexes of nodes or edges of social graph 228.

The social graph 228 may be accessed by a social-networking server 226,client system 210, third-party system (e.g., the translation server224), or any other approved system or device for suitable applications.

A user node 230 may correspond to a user of the social-networkingsystem. A user may be an individual (human user), an entity (e.g., anenterprise, business, or third-party application), or a group (e.g., ofindividuals or entities) that interacts or communicates with or over thesocial-networking system. In exemplary embodiments, when a userregisters for an account with the social-networking system, thesocial-networking system may create a user node 230 corresponding to theuser, and store the user node 30 in one or more data stores. Users anduser nodes 230 described herein may, where appropriate, refer toregistered users and user nodes 230 associated with registered users. Inaddition or as an alternative, users and user nodes 230 described hereinmay, where appropriate, refer to users that have not registered with thesocial-networking system. In particular embodiments, a user node 230 maybe associated with information provided by a user or informationgathered by various systems, including the social-networking system. Asan example and not by way of limitation, a user may provide their name,profile picture, contact information, birth date, sex, marital status,family status, employment, education background, preferences, interests,or other demographic information. In particular embodiments, a user node230 may be associated with one or more data objects corresponding toinformation associated with a user. In particular embodiments, a usernode 230 may correspond to one or more webpages. A user node 230 may beassociated with a unique user identifier for the user in thesocial-networking system.

In particular embodiments, a concept node 232 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-network service or a third-partywebsite associated with a web-application server); an entity (such as,for example, a person, business, group, sports team, or celebrity); aresource (such as, for example, an audio file, video file, digitalphoto, text file, structured document, or application) which may belocated within the social-networking system or on an external server,such as a web-application server; real or intellectual property (suchas, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node232 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system. As an example and not by way of limitation,information of a concept may include a name or a title; one or moreimages (e.g., an image of the cover page of a book); a location (e.g.,an address or a geographical location); a website (which may beassociated with a URL); contact information (e.g., a phone number or anemail address); other suitable concept information; or any suitablecombination of such information. In particular embodiments, a conceptnode 232 may be associated with one or more data objects correspondingto information associated with concept node 232. In particularembodiments, a concept node 232 may correspond to one or more webpages.

In particular embodiments, a node in social graph 228 may represent orbe represented by a webpage (which may be referred to as a “profilepage”). Profile pages may be hosted by or accessible to thesocial-networking system. Profile pages may also be hosted onthird-party websites associated with a third-party server. As an exampleand not by way of limitation, a profile page corresponding to aparticular external webpage may be the particular external webpage andthe profile page may correspond to a particular concept node 232.Profile pages may be viewable by all or a selected subset of otherusers. As an example and not by way of limitation, a user node 230 mayhave a corresponding user-profile page in which the corresponding usermay add content, make declarations, or otherwise express himself orherself. A business page such as business page 205 may comprise auser-profile page for a commerce entity. As another example and not byway of limitation, a concept node 232 may have a correspondingconcept-profile page in which one or more users may add content, makedeclarations, or express themselves, particularly in relation to theconcept corresponding to concept node 232.

In particular embodiments, a concept node 232 may represent athird-party webpage or resource hosted by a third-party system. Thethird-party webpage or resource may include, among other elements,content, a selectable or other icon, or other inter-actable object(which may be implemented, for example, in JavaScript, AJAX, or PHPcodes) representing an action or activity. As an example and not by wayof limitation, a third-party webpage may include a selectable icon suchas “like,” “check in,” “eat,” “recommend,” or another suitable action oractivity. A user viewing the third-party webpage may perform an actionby selecting one of the icons (e.g., “eat”), causing a client system tosend to the social-networking system a message indicating the user'saction. In response to the message, the social-networking system maycreate an edge (e.g., an “eat” edge) between a user node 230corresponding to the user and a concept node 232 corresponding to thethird-party webpage or resource and store edge 234 in one or more datastores.

In particular embodiments, a pair of nodes in social graph 228 may beconnected to each other by one or more edges 234. An edge 234 connectinga pair of nodes may represent a relationship between the pair of nodes.In particular embodiments, an edge 234 may include or represent one ormore data objects or attributes corresponding to the relationshipbetween a pair of nodes. As an example and not by way of limitation, afirst user may indicate that a second user is a “friend” of the firstuser. In response to this indication, the social-networking system maysend a “friend request” to the second user. If the second user confirmsthe “friend request,” the social-networking system may create an edge234 connecting the first user's user node 230 to the second user's usernode 230 in social graph 228 and store edge 234 as social-graphinformation in one or more data stores. In the example of FIG. 7, socialgraph 228 includes an edge 234 indicating a friend relation between usernodes 230 of user “Amanda” and user “Dorothy.” Although this disclosuredescribes or illustrates particular edges 234 with particular attributesconnecting particular user nodes 230, this disclosure contemplates anysuitable edges 234 with any suitable attributes connecting user nodes230. As an example and not by way of limitation, an edge 234 mayrepresent a friendship, family relationship, business or employmentrelationship, fan relationship, follower relationship, visitorrelationship, subscriber relationship, superior/subordinaterelationship, reciprocal relationship, non-reciprocal relationship,another suitable type of relationship, or two or more suchrelationships. Moreover, although this disclosure generally describesnodes as being connected, this disclosure also describes users orconcepts as being connected. Herein, references to users or conceptsbeing connected may, where appropriate, refer to the nodes correspondingto those users or concepts being connected in social graph 228 by one ormore edges 234.

In particular embodiments, an edge 234 between a user node 230 and aconcept node 232 may represent a particular action or activity performedby a user associated with user node 230 toward a concept associated witha concept node 232. As an example and not by way of limitation, asillustrated in FIG. 7, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “watched” a concept, each of whichmay correspond to a edge type or subtype. A concept-profile pagecorresponding to a concept node 232 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, the social-networking system may create a “favorite”edge or a “check in” edge in response to a user's action correspondingto a respective action. As another example and not by way of limitation,a user (user “Carla”) may listen to a particular song (“Across the Sea”)using a particular application (SPOTIFY, which is an online musicapplication). In this case, the social-networking system may create a“listened” edge 234 and a “used” edge (as illustrated in FIG. 7) betweenuser nodes 230 corresponding to the user and concept nodes 232corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system may create a “played” edge 234 (as illustratedin FIG. 7) between concept nodes 232 corresponding to the song and theapplication to indicate that the particular song was played by theparticular application. In this case, “played” edge 234 corresponds toan action performed by an external application (SPOTIFY) on an externalaudio file (the song “Across the Sea”). Although this disclosuredescribes particular edges 234 with particular attributes connectinguser nodes 230 and concept nodes 232, this disclosure contemplates anysuitable edges 234 with any suitable attributes connecting user nodes230 and concept nodes 232. Moreover, although this disclosure describesedges between a user node 230 and a concept node 232 representing asingle relationship, this disclosure contemplates edges between a usernode 230 and a concept node 232 representing one or more relationships.As an example and not by way of limitation, an edge 234 may representboth that a user likes and has used at a particular concept.Alternatively, another edge 234 may represent each type of relationship(or multiples of a single relationship) between a user node 230 and aconcept node 232 (as illustrated in FIG. 7 between user node 230 foruser “Edwin” and concept node 232 for “SPOTIFY”).

In particular embodiments, the social-networking system may create anedge 234 between a user node 230 and a concept node 232 in social graph228. As an example and not by way of limitation, a user viewing aconcept-profile page (such as, for example, by using a web browser or aspecial-purpose application hosted by the user's client system) mayindicate that he or she likes the concept represented by the conceptnode 232 by clicking or selecting a “Like” icon, which may cause theuser's client system to send to the social-networking system a messageindicating the user's liking of the concept associated with theconcept-profile page. In response to the message, the social-networkingsystem may create an edge 234 between user node 230 associated with theuser and concept node 232, as illustrated by “like” edge 234 between theuser and concept node 232. In particular embodiments, thesocial-networking system may store an edge 234 in one or more datastores. In particular embodiments, an edge 234 may be automaticallyformed by the social-networking system in response to a particular useraction. As an example and not by way of limitation, if a first useruploads a picture, watches a movie, or listens to a song, an edge 234may be formed between user node 230 corresponding to the first user andconcept nodes 232 corresponding to those concepts. Although thisdisclosure describes forming particular edges 234 in particular manners,this disclosure contemplates forming any suitable edges 234 in anysuitable manner.

The social graph 228 may further comprise a plurality of product nodes.Product nodes may represent particular products that may be associatedwith a particular business. A business may provide a product catalog toa consumer-to-business service and the consumer-to-business service maytherefore represent each of the products within the product in thesocial graph 228 with each product being in a distinct product node. Aproduct node may comprise information relating to the product, such aspricing information, descriptive information, manufacturer information,availability information, and other relevant information. For example,each of the items on a menu for a restaurant may be represented withinthe social graph 228 with a product node describing each of the items. Aproduct node may be linked by an edge to the business providing theproduct. Where multiple businesses provide a product, each business mayhave a distinct product node associated with its providing of theproduct or may each link to the same product node. A product node may belinked by an edge to each user that has purchased, rated, owns,recommended, or viewed the product, with the edge describing the natureof the relationship (e.g., purchased, rated, owns, recommended, viewed,or other relationship). Each of the product nodes may be associated witha graph id and an associated merchant id by virtue of the linkedmerchant business. Products available from a business may therefore becommunicated to a user by retrieving the available product nodes linkedto the user node for the business within the social graph 228. Theinformation for a product node may be manipulated by thesocial-networking system as a product object that encapsulatesinformation regarding the referenced product.

As such, the social graph 228 may be used to infer shared interests,shared experiences, or other shared or common attributes of two or moreusers of a social-networking system. For instance, two or more userseach having an edge to a common business, product, media item,institution, or other entity represented in the social graph 228 mayindicate a shared relationship with that entity, which may be used tosuggest customization of a use of a social-networking system, includinga messaging system, for one or more users.

Computer-Related Embodiments

The above-described methods may be embodied as instructions on acomputer readable medium or as part of a computing architecture. FIG. 8illustrates an embodiment of an exemplary computing architecture 236suitable for implementing various embodiments as previously described.In one embodiment, the computing architecture 236 may comprise or beimplemented as part of an electronic device. Examples of an electronicdevice may include those described with reference to FIG. 8, amongothers. The embodiments are not limited in this context.

As used in this application, the terms “system” and “component” areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution, examples of which are provided by the exemplary computingarchitecture 236. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, a hard disk drive,multiple storage drives (of optical and/or magnetic storage medium), anobject, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution, and a component canbe localized on one computer and/or distributed between two or morecomputers. Further, components may be communicatively coupled to eachother by various types of communications media to coordinate operations.The coordination may involve the unidirectional or bi-directionalexchange of information. For instance, the components may communicateinformation in the form of signals communicated over the communicationsmedia. The information can be implemented as signals allocated tovarious signal lines. In such allocations, each message is a signal.Further embodiments, however, may alternatively employ data messages.Such data messages may be sent across various connections. Exemplaryconnections include parallel interfaces, serial interfaces, and businterfaces.

The computing architecture 236 includes various common computingelements, such as one or more processors, multi-core processors,co-processors, memory units, chipsets, controllers, peripherals,interfaces, oscillators, timing devices, video cards, audio cards,multimedia input/output (I/O) components, power supplies, and so forth.The embodiments, however, are not limited to implementation by thecomputing architecture 236.

As shown in FIG. 8, the computing architecture 236 comprises aprocessing unit 240, a system memory 242 and a system bus 244. Theprocessing unit 240 can be any of various commercially availableprocessors, including without limitation an AMD® Athlon®, Duron® andOpteron® processors; ARM® application, embedded and secure processors;IBM® and Motorola® DragonBall® and PowerPC® processors; IBM and Sony®Cell processors; Intel® Celeron®, Core (2) Duo®, Itanium®, Pentium®,Xeon®, and XScale® processors; and similar processors. Dualmicroprocessors, multi-core processors, and other multi-processorarchitectures may also be employed as the processing unit 240.

The system bus 244 provides an interface for system componentsincluding, but not limited to, the system memory 242 to the processingunit 240. The system bus 244 can be any of several types of busstructure that may further interconnect to a memory bus (with or withouta memory controller), a peripheral bus, and a local bus using any of avariety of commercially available bus architectures. Interface adaptersmay connect to the system bus 244 via a slot architecture. Example slotarchitectures may include without limitation Accelerated Graphics Port(AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA),Micro Channel Architecture (MCA), NuBus, Peripheral ComponentInterconnect (Extended) (PCI(X)), PCI Express, Personal Computer MemoryCard International Association (PCMCIA), and the like.

The computing architecture 236 may comprise or implement variousarticles of manufacture. An article of manufacture may comprise acomputer-readable storage medium to store logic. Examples of acomputer-readable storage medium may include any tangible media capableof storing electronic data, including volatile memory or non-volatilememory, removable or non-removable memory, erasable or non-erasablememory, writeable or re-writeable memory, and so forth. Examples oflogic may include executable computer program instructions implementedusing any suitable type of code, such as source code, compiled code,interpreted code, executable code, static code, dynamic code,object-oriented code, visual code, and the like. Embodiments may also beat least partly implemented as instructions contained in or on anon-transitory computer-readable medium, which may be read and executedby one or more processors to enable performance of the operationsdescribed herein.

The system memory 242 may include various types of computer-readablestorage media in the form of one or more higher speed memory units, suchas read-only memory (ROM), random-access memory (RAM), dynamic RAM(DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), staticRAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, polymermemory such as ferroelectric polymer memory, ovonic memory, phase changeor ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS)memory, magnetic or optical cards, an array of devices such as RedundantArray of Independent Disks (RAID) drives, solid state memory devices(e.g., USB memory, solid state drives (SSD) and any other type ofstorage media suitable for storing information. In the illustratedembodiment shown in FIG. 8, the system memory 242 can includenon-volatile memory 246 and/or volatile memory 248. A basic input/outputsystem (BIOS) can be stored in the non-volatile memory 246.

The computer 238 may include various types of computer-readable storagemedia in the form of one or more lower speed memory units, including aninternal (or external) hard disk drive (HDD) 250, a magnetic floppy diskdrive (FDD) 252 to read from or write to a removable magnetic disk 254,and an optical disk drive 256 to read from or write to a removableoptical disk 258 (e.g., a CD-ROM or DVD). The HDD 250, FDD 252 andoptical disk drive 256 can be connected to the system bus 244 by a HDDinterface 260, an FDD interface 262 and an optical drive interface 264,respectively. The HDD interface 260 for external drive implementationscan include at least one or both of Universal Serial Bus (USB) and IEEE694 interface technologies.

The drives and associated computer-readable media provide volatileand/or nonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For example, a number of program modules canbe stored in the drives and memory units 246, 248, including anoperating system 266, one or more application programs 268, otherprogram modules 270, and program data 272. In one embodiment, the one ormore application programs 268, other program modules 270, and programdata 272 can include, for example, the various applications and/orcomponents of the system 30.

A user can enter commands and information into the computer 238 throughone or more wire/wireless input devices, for example, a keyboard 274 anda pointing device, such as a mouse 276. Other input devices may includemicrophones, infra-red (IR) remote controls, radio-frequency (RF) remotecontrols, game pads, stylus pens, card readers, dongles, finger printreaders, gloves, graphics tablets, joysticks, keyboards, retina readers,touch screens (e.g., capacitive, resistive, etc.), trackballs,trackpads, sensors, styluses, and the like. These and other inputdevices are often connected to the processing unit 504 through an inputdevice interface 278 that is coupled to the system bus 244, but can beconnected by other interfaces such as a parallel port, IEEE 694 serialport, a game port, a USB port, an IR interface, and so forth.

A monitor 280 or other type of display device is also connected to thesystem bus 244 via an interface, such as a video adaptor 282. Themonitor 280 may be internal or external to the computer 238. In additionto the monitor 280, a computer typically includes other peripheraloutput devices, such as speakers, printers, and so forth.

The computer 238 may operate in a networked environment using logicalconnections via wire and/or wireless communications to one or moreremote computers, such as a remote computer 284. The remote computer 284can be a workstation, a server computer, a router, a personal computer,portable computer, microprocessor-based entertainment appliance, a peerdevice or other common network node, and typically includes many or allof the elements described relative to the computer 238, although, forpurposes of brevity, only a memory/storage device 286 is illustrated.The logical connections depicted include wire/wireless connectivity to alocal area network (LAN) 288 and/or larger networks, for example, a widearea network (WAN) 290. Such LAN and WAN networking environments arecommonplace in offices and companies, and facilitate enterprise-widecomputer networks, such as intranets, all of which may connect to aglobal communications network, for example, the Internet.

When used in a LAN networking environment, the computer 238 is connectedto the LAN 288 through a wire and/or wireless communication networkinterface or adaptor 292. The adaptor 292 can facilitate wire and/orwireless communications to the LAN 288, which may also include awireless access point disposed thereon for communicating with thewireless functionality of the adaptor 292.

When used in a WAN networking environment, the computer 238 can includea modem 294, or is connected to a communications server on the WAN 290,or has other means for establishing communications over the WAN 290,such as by way of the Internet. The modem 294, which can be internal orexternal and a wire and/or wireless device, connects to the system bus244 via the input device interface 278. In a networked environment,program modules depicted relative to the computer 238, or portionsthereof, can be stored in the remote memory/storage device 286. It willbe appreciated that the network connections shown are exemplary andother means of establishing a communications link between the computerscan be used.

The computer 238 is operable to communicate with wire and wirelessdevices or entities using the IEEE 802 family of standards, such aswireless devices operatively disposed in wireless communication (e.g.,IEEE 802.13 over-the-air modulation techniques). This includes at leastWi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wirelesstechnologies, among others. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices. Wi-Fi networks use radiotechnologies called IEEE 802.13x (a, b, g, n, etc.) to provide secure,reliable, fast wireless connectivity. A Wi-Fi network can be used toconnect computers to each other, to the Internet, and to wire networks(which use IEEE 802.3-related media and functions).

General Notes on Terminology

Some embodiments may be described using the expression “one embodiment”or “an embodiment” along with their derivatives. These terms mean that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment.Moreover, unless otherwise noted the features described above arerecognized to be usable together in any combination. Thus, any featuresdiscussed separately may be employed in combination with each otherunless it is noted that the features are incompatible with each other.

With general reference to notations and nomenclature used herein, thedetailed descriptions herein may be presented in terms of programprocedures executed on a computer or network of computers. Theseprocedural descriptions and representations are used by those skilled inthe art to most effectively convey the substance of their work to othersskilled in the art.

A procedure is here, and generally, conceived to be a self-consistentsequence of operations leading to a desired result. These operations arethose requiring physical manipulations of physical quantities. Usually,though not necessarily, these quantities take the form of electrical,magnetic or optical signals capable of being stored, transferred,combined, compared, and otherwise manipulated. It proves convenient attimes, principally for reasons of common usage, to refer to thesesignals as bits, values, elements, symbols, characters, terms, numbers,or the like. It should be noted, however, that all of these and similarterms are to be associated with the appropriate physical quantities andare merely convenient labels applied to those quantities.

Further, the manipulations performed are often referred to in terms,such as adding or comparing, which are commonly associated with mentaloperations performed by a human operator. No such capability of a humanoperator is necessary, or desirable in most cases, in any of theoperations described herein, which form part of one or more embodiments.Rather, the operations are machine operations. Useful machines forperforming operations of various embodiments include general purposedigital computers or similar devices.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. These terms are notnecessarily intended as synonyms for each other. For example, someembodiments may be described using the terms “connected” and/or“coupled” to indicate that two or more elements are in direct physicalor electrical contact with each other. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other.

Various embodiments also relate to apparatus or systems for performingthese operations. This apparatus may be specially constructed for therequired purpose or it may comprise a general purpose computer asselectively activated or reconfigured by a computer program stored inthe computer. The procedures presented herein are not inherently relatedto a particular computer or other apparatus. Various general purposemachines may be used with programs written in accordance with theteachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these machines will appear from thedescription given.

It is emphasized that the Abstract of the Disclosure is provided toallow a reader to quickly ascertain the nature of the technicaldisclosure. It is submitted with the understanding that it will not beused to interpret or limit the scope or meaning of the claims. Inaddition, in the foregoing Detailed Description, it can be seen thatvarious features are grouped together in a single embodiment for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the claimedembodiments require more features than are expressly recited in eachclaim. Rather, as the following claims reflect, inventive subject matterlies in less than all features of a single disclosed embodiment. Thusthe following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separateembodiment. In the appended claims, the terms “including” and “in which”are used as the plain-English equivalents of the respective terms“comprising” and “wherein,” respectively. Moreover, the terms “first,”“second,” “third,” and so forth, are used merely as labels, and are notintended to impose numerical requirements on their objects.

What has been described above includes examples of the disclosedarchitecture. It is, of course, not possible to describe everyconceivable combination of components and/or methodologies, but one ofordinary skill in the art may recognize that many further combinationsand permutations are possible. Accordingly, the novel architecture isintended to embrace all such alterations, modifications and variationsthat fall within the spirit and scope of the appended claims.

1. A method comprising: (a) generating a first candidate translation anda second candidate translation using a machine translation system; (b)causing the first candidate translation to be provided to a first groupof users; (c) causing the second candidate translation to be provided toa second group of users; (d) receiving one or more indications ofengagement with the first candidate translation and the second candidatetranslation; (e) determining, based on the one or more indications ofengagement, that the first candidate translation was favored over thesecond candidate translation; and (f) modifying the machine translationsystem in favor of the first candidate translation.
 2. The method ofclaim 1, wherein operation (b) comprises surfacing the first candidatetranslation to the first group of users on a social network, andoperation (c) comprises surfacing the second candidate translation tothe second group of users on the social network.
 3. The method of claim2, wherein engagement with the first translation and engagement with thesecond translation are measure based on at least one of: a number oftimes that the respective translations are liked on the social network,a number of times that the respective translations are shared on thesocial network, or a number of comments written with respect to therespective translations.
 4. The method of claim 1, wherein engagementwith the first translation and engagement with the second translationare measured by querying the first group of users and the second groupof users as to whether the respective translations are understandable oruseable.
 5. The method of claim 1, wherein the translation systemcomprises a translation model that generates one or more destinationlanguage hypotheses for source language input material based on atranslation model parameter, and operation (f) comprises modifying thetranslation model parameter.
 6. The method of claim 1, wherein thetranslation system comprises a language model that selects between twoor more destination language hypotheses for source language inputmaterial based on a language model parameter, and operation (f)comprises modifying the language model parameter.
 7. The method of claim1, further comprising repeating operation (a)-(f) using the modifiedmachine translation system to further tune the machine translationsystem.
 8. A non-transitory computer-readable medium storinginstructions that, when executed by one or more processors, cause theone or more processors to: (a) generate a first candidate translationand a second candidate translation using a machine translation system;(b) cause the first candidate translation to be provided to a firstgroup of users; (c) cause the second candidate translation to beprovided to a second group of users; (d) receive one or more indicationsof engagement with the first candidate translation and the secondcandidate translation; (e) determine, based on the one or moreindications of engagement, that the first candidate translation wasfavored over the second candidate translation; and (f) modify themachine translation system in favor of the first candidate translation.9. The medium of claim 8, wherein operation (b) comprises surfacing thefirst candidate translation to the first group of users on a socialnetwork, and operation (c) comprises surfacing the second candidatetranslation to the second group of users on the social network.
 10. Themedium of claim 9, wherein engagement with the first translation andengagement with the second translation are measure based on at least oneof: a number of times that the respective translations are liked on thesocial network, a number of times that the respective translations areshared on the social network, or a number of comments written withrespect to the respective translations.
 11. The medium of claim 8,wherein engagement with the first translation and engagement with thesecond translation are measured by querying the first group of users andthe second group of users as to whether the respective translations areunderstandable or useable.
 12. The medium of claim 8, wherein thetranslation system comprises a translation model that generates one ormore destination language hypotheses for source language input materialbased on a translation model parameter, and operation (f) comprisesmodifying the translation model parameter.
 13. The medium of claim 8,wherein the translation system comprises a language model that selectsbetween two or more destination language hypotheses for source languageinput material based on a language model parameter, and operation (f)comprises modifying the language model parameter.
 14. The medium ofclaim 8, further storing instructions for repeating operations (a)-(f)using the modified machine translation system to further tune themachine translation system.
 15. An apparatus comprising: anon-transitory computer-readable medium configured to store one or moremodels used by a machine translation system; a processor configured to:(a) generate a first candidate translation and a second candidatetranslation using the machine translation system, (b) cause the firstcandidate translation to be provided to a first group of users, (c)cause the second candidate translation to be provided to a second groupof users, (d) receive one or more indications of engagement with thefirst candidate translation and the second candidate translation, (e)determine, based on the one or more indications of engagement, that thefirst candidate translation was favored over the second candidatetranslation, and (f) modify the machine translation system in favor ofthe first candidate translation.
 16. The apparatus of claim 15, whereinoperation (b) comprises surfacing the first candidate translation to thefirst group of users on a social network, and operation (c) comprisessurfacing the second candidate translation to the second group of userson the social network.
 17. The apparatus of claim 10, wherein engagementwith the first translation and engagement with the second translationare measure based on at least one of: a number of times that therespective translations are liked on the social network, a number oftimes that the respective translations are shared on the social network,or a number of comments written with respect to the respectivetranslations.
 18. The apparatus of claim 15, wherein engagement with thefirst translation and engagement with the second translation aremeasured by querying the first group of users and the second group ofusers as to whether the respective translations are understandable oruseable.
 19. The apparatus of claim 15, wherein the one or more modelscomprise a translation model that generates one or more destinationlanguage hypotheses for source language input material based on atranslation model parameter, and operation (f) comprises modifying thetranslation model parameter.
 20. The apparatus of claim 15, wherein theone or more models comprise a language model that selects between two ormore destination language hypotheses for source language input materialbased on a language model parameter, and operation (f) comprisesmodifying the language model parameter.